The Data You Need for Effective Personalization

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Kabir is CEO at Amperity, and a technology entrepreneur and senior executive with experience at many stages of the lifecycle: start-up/bootstrap, venture funding, M&A, and IPO experience. He co- founded Amperity in January 2016 with the vision of using data and software to unleash the potential of marketers and analysts at the world’s most admired consumer brands. Kabir’s previous company, Appature, developed a relationship marketing platform that enables brands to deepen customer relationships through an integrated data infrastructure, multi- channel campaign toolset, and innovative analytics engine. In recognition of the company’s success, Kabir was named one of BusinessWeek’s “Best Young Tech Entrepreneurs of 2009” and was also named a 2011 Puget Sound Business Journal “40 Under 40" honoree. In 2012, he was awarded the U.S. SBA “Young Entrepreneur of the Year” award.

Kabir Shahani, CEO & Co-founder of Amperity, outlines in detail what companies need to do to harness the power of their customer data

Take a look at the latest MarTech Infographic. There are logos from 6,242 vendors trying to help brands personalize their websites, target audiences more efficiently, and optimize every distinct touchpoint and interaction. Personalization technologies have never been more advanced.

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And consumers have never been more receptive. Last year Accenture reported that 41% of consumers switched brands last year due to a lack of personalization and trust. Our analysts here at Boston Consulting Group predict a massive $800 billion revenue shift, over the next 5 years, to the 15% of brands that get personalization right. Consumers want personalization.

So why isn’t every brand jumping on the personalization train and riding off into their big piles of cash? Because their customer data isn’t unified or usable.

Do you really need all of your customer data, intelligently unified, to get personalization right? Yes, you do. How many times have your ads followed your customers around the internet, after they’ve already purchased the product, wasting ad dollars and annoying customers? How often do you send the exact same email to all four of a customer’s email addresses, treating them like 4 separate people and urging them to unsubscribe? Is your website personalized to your customers’ actual tastes, not just the last product they happened to browse, optimizing it for conversions and revenue? If the answer is ‘no’ to any of these questions, you have a customer data problem -- one that’s costing you money, customer loyalty, and long-term growth.

And while the world’s most loved brands are struggling, Internet-only brands, whose customer data capabilities were built for personalization, are redefining what’s possible. Will you be in the 15% of brands that get personalization right? If you want to be, the path forward is to rethink and rebuild your customer data management capabilities from the ground up.

Building Customer Data Intelligence Capabilities that Work

There are three key steps to making your disparate customer data usable for personalization. First, you need it all in one, central place. Then you need to resolve customer identities to form individual customer profiles. Finally, you need to take action on the data by using it in all your customer touchpoints.

Step One: Co-locate Your Data

The first step is to bring all your customer data in a centralized data store. At this point, you just want it co-located together. Many teams make the mistake of using an EDW or a CRM tool, which has a hard-coded schema and requires months of data transformations to get data in. This approach is rigid and breaks whenever you want to add or change data sources, which is a huge waste of your team’s time and your marketing budget, ultimately leaving you without access to the data you need, when you need it.

Instead, co-locate your raw data in a flexible, scalable data store without a predefined schema (think data lake). This means no lengthy transformations and fast and easy setup. With this approach, new sources can be easily integrated at any time, future-proofing your investments and giving you the freedom to always choose best-of-breed technologies as they become available.

The data store must also be scalable because data from all your systems can quickly add up to billions of records. Speed of ingestion also matters, which is again why a scalable platform and raw data ingestion are keys to being able to use your data while it is still current.

Step Two: Connect Your Data

Once you’ve brought all your data into a flexible, scalable, and centralized data store, you need to unify it into rich, cross-source customer profiles.

In a perfect world, all of your datasets would share a unifying key (like a social security number or a thumbprint). Using a simple join, you could connect all your data and build profiles. In the real world, however, the majority of your data sources don’t have these keys. Some brands try to solve for this using static business rules, but this approach is lossy, inaccurate, and hard to maintain as data sources change over time.

Instead, top brands do like the Googles and the Amazons do. They use machine learning.

Machine learning algorithms, which have been trained on massive amounts of customer data, scour your records for matches in a process called intelligent identity resolution. Clusters of records across datasets are then accurately linked, based on the unique features of the data, not the best guesses of your team. This approach results in huge lifts in the completeness of profiles. And the more complete your profiles are, the better your personalization becomes.

Step Three: Syndicate Your Data

Finally, you need to fuel all your various systems of engagement (email, social, site, etc) with rich, unified customer data. This requires integrations with all your external systems and a central data store that never traps your data. Like when data was brought in, it must now be syndicated out using integrations built for speed and scale.

The last comment on using data for personalization: personalization requires knowing customers intimately based on all the data they share with you. As you begin to experiment with new personalization initiatives, you will enrich your understanding of customers, based on how customers respond to your efforts. This data should also be circulated back into your customer data intelligence capabilities for iterative improvements and optimization over time.

In summary, some important questions to consider:

Can you easily bring all your disparate customer data into a flexible and centralized data store?

Can you accurately resolve customer identities across all your data, even when your systems lack unifying keys?

And last, can you drive meaningful personalization to use your existing systems of engagement powered by rich and unified customer profiles?

If the answer to any of these questions is ‘no’, it’s time to invest in your customer data intelligence capabilities. Many brands are pursuing unified and usable customer data by some means, but most have yet to find success. With a system built from the ground up for scale, accuracy, and completeness, you can ensure that yours in the 15% of brands that get personalization right.